{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#51CTO课程频道：http://edu.51cto.com/lecturer/index/user_id-12330098.html\n",
    "#优酷频道：http://i.youku.com/sdxxqbf\n",
    "#微信公众号：深度学习与神经网络\n",
    "#Github：https://github.com/Qinbf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\n异或\\n0^0 = 0\\n0^1 = 1\\n1^0 = 1\\n1^1 = 0\\n'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "异或\n",
    "0^0 = 0\n",
    "0^1 = 1\n",
    "1^0 = 1\n",
    "1^1 = 0\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.08535332 -0.84234456 -0.74029992]\n"
     ]
    }
   ],
   "source": [
    "#输入数据\n",
    "X = np.array([[1,0,0],\n",
    "              [1,0,1],\n",
    "              [1,1,0],\n",
    "              [1,1,1]])\n",
    "#标签\n",
    "Y = np.array([-1,1,1,-1])\n",
    "#权值初始化，1行3列，取值范围-1到1\n",
    "W = (np.random.random(3)-0.5)*2\n",
    "print(W)\n",
    "#学习率设置\n",
    "lr = 0.11\n",
    "#计算迭代次数\n",
    "n = 0\n",
    "#神经网络输出\n",
    "O = 0\n",
    "\n",
    "def update():\n",
    "    global X,Y,W,lr,n\n",
    "    n+=1\n",
    "    O = np.sign(np.dot(X,W.T))\n",
    "    W_C = lr*((Y-O.T).dot(X))/int(X.shape[0])\n",
    "    W = W + W_C"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.02464668 -0.78734456 -0.68529992]\n",
      "1\n",
      "[ 0.07964668 -0.73234456 -0.63029992]\n",
      "2\n",
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      "[ 0.07964668 -0.01734456  0.02970008]\n",
      "966\n",
      "[-0.03035332 -0.07234456 -0.02529992]\n",
      "967\n",
      "[ 0.07964668 -0.01734456  0.02970008]\n",
      "968\n",
      "[-0.03035332 -0.07234456 -0.02529992]\n",
      "969\n",
      "[ 0.07964668 -0.01734456  0.02970008]\n",
      "970\n",
      "[-0.03035332 -0.07234456 -0.02529992]\n",
      "971\n",
      "[ 0.07964668 -0.01734456  0.02970008]\n",
      "972\n",
      "[-0.03035332 -0.07234456 -0.02529992]\n",
      "973\n",
      "[ 0.07964668 -0.01734456  0.02970008]\n",
      "974\n",
      "[-0.03035332 -0.07234456 -0.02529992]\n",
      "975\n",
      "[ 0.07964668 -0.01734456  0.02970008]\n",
      "976\n",
      "[-0.03035332 -0.07234456 -0.02529992]\n",
      "977\n",
      "[ 0.07964668 -0.01734456  0.02970008]\n",
      "978\n",
      "[-0.03035332 -0.07234456 -0.02529992]\n",
      "979\n",
      "[ 0.07964668 -0.01734456  0.02970008]\n",
      "980\n",
      "[-0.03035332 -0.07234456 -0.02529992]\n",
      "981\n",
      "[ 0.07964668 -0.01734456  0.02970008]\n",
      "982\n",
      "[-0.03035332 -0.07234456 -0.02529992]\n",
      "983\n",
      "[ 0.07964668 -0.01734456  0.02970008]\n",
      "984\n",
      "[-0.03035332 -0.07234456 -0.02529992]\n",
      "985\n",
      "[ 0.07964668 -0.01734456  0.02970008]\n",
      "986\n",
      "[-0.03035332 -0.07234456 -0.02529992]\n",
      "987\n",
      "[ 0.07964668 -0.01734456  0.02970008]\n",
      "988\n",
      "[-0.03035332 -0.07234456 -0.02529992]\n",
      "989\n",
      "[ 0.07964668 -0.01734456  0.02970008]\n",
      "990\n",
      "[-0.03035332 -0.07234456 -0.02529992]\n",
      "991\n",
      "[ 0.07964668 -0.01734456  0.02970008]\n",
      "992\n",
      "[-0.03035332 -0.07234456 -0.02529992]\n",
      "993\n",
      "[ 0.07964668 -0.01734456  0.02970008]\n",
      "994\n",
      "[-0.03035332 -0.07234456 -0.02529992]\n",
      "995\n",
      "[ 0.07964668 -0.01734456  0.02970008]\n",
      "996\n",
      "[-0.03035332 -0.07234456 -0.02529992]\n",
      "997\n",
      "[ 0.07964668 -0.01734456  0.02970008]\n",
      "998\n",
      "[-0.03035332 -0.07234456 -0.02529992]\n",
      "999\n",
      "[ 0.07964668 -0.01734456  0.02970008]\n",
      "1000\n",
      "k= 0.583990439614\n",
      "d= -2.6816989411\n"
     ]
    },
    {
     "data": {
      "image/png": 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UEJg8Ge64AyZMgK5ds55MJc4FhJJU7qZPh4suSg2B1q1TW+C442DFFbOeTGXCMCBJ5Wre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\ngCRJOff/ANJMrflY+B/qAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x265f7a65dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for _ in range(1000):\n",
    "    update()#更新权值\n",
    "    print(W)#打印当前权值\n",
    "    print(n)#打印迭代次数\n",
    "    O = np.sign(np.dot(X,W.T))#计算当前输出\n",
    "    if(O == Y.T).all(): #如果实际输出等于期望输出，模型收敛，循环结束\n",
    "        print('Finished')\n",
    "        print('epoch:',n)\n",
    "        break\n",
    "\n",
    "#正样本\n",
    "x1 = [0,1]\n",
    "y1 = [1,0]\n",
    "#负样本\n",
    "x2 = [0,1]\n",
    "y2 = [0,1]\n",
    "\n",
    "#计算分界线的斜率以及截距\n",
    "k = -W[1]/W[2]\n",
    "d = -W[0]/W[2]\n",
    "print('k=',k)\n",
    "print('d=',d)\n",
    "\n",
    "xdata = np.linspace(-2,5)\n",
    "\n",
    "plt.figure()\n",
    "plt.plot(xdata,xdata*k+d,'r')\n",
    "plt.plot(x1,y1,'bo')\n",
    "plt.plot(x2,y2,'yo')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
